Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden
DOI: 10.1109/tits.2019.2931797
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the critical need to understand cross-cultural differences in car-following behavior to facilitate the development of autonomous driving systems and active safety features that function effectively in mixed traffic conditions globally. While previous research has examined car-following dynamics within single regions, few studies have compared these behaviors across different countries using real-world field data. The authors aim to identify similarities and differences in driver speed control strategies by analyzing safety indicators—specifically time gap, gap distance, and time to collision (TTC)—using datasets from China and Sweden. The research utilizes two independent field test datasets. The China dataset comprises data from 12 male drivers (mean age 41.8) driving instrumented Volkswagen Tourans on national highways and expressways in Zhejiang province. The Sweden dataset is derived from the euroFOT project, involving 24 drivers (11 female, 13 male; mean age 47.3) driving Volvo vehicles in Gothenburg over one year. Car-following events were extracted using specific criteria, including a relative angle of less than 2 degrees, host vehicle speeds above 10 km/h, and time gaps under 6 seconds. The analysis compares the distribution of safety indicators and their relationships with host vehicle speed across both datasets. The results reveal distinct behavioral patterns between the two groups. The highest frequency of gap distance falls within the 20–30 meter range for both countries, indicating a shared preference for this physical spacing. However, Swedish drivers consistently maintain shorter time gaps than Chinese drivers; the peak frequency for time gap is 1.0–1.5 seconds in Sweden compared to 1.5–2.0 seconds in China. Statistical analysis shows that mean gap distance increases significantly with host vehicle speed in both datasets, whereas mean time gap remains relatively stable regardless of speed, hovering around 2.0 seconds for Chinese drivers and 1.5 seconds for Swedish drivers. Furthermore, time gap is identified as a more reliable indicator than gap distance because it is less sensitive to speed variations. The study also finds that TTC is steady at low speeds (<50 km/h), while time gap is more consistent at high speeds (>90 km/h). The significance of these findings lies in the recommendation for context-specific safety indicators. The authors conclude that time gap is the preferred metric for analyzing car-following behavior in high-speed conditions, while a combination of time gap and TTC is recommended for low-speed urban environments. These insights provide essential empirical evidence for calibrating automated vehicle algorithms and active safety warnings to accommodate diverse driving styles, ensuring that systems developed in one region can adapt safely to the behavioral norms of another.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: behavioral performance data